Methods and apparatus to model on/off states of media presentation devices based on return path data

ABSTRACT

Methods and apparatus to model on/off states of media presentation devices based on return path data are disclosed. An apparatus includes a memory and processor circuitry to execute instructions stored in the memory to: generate a probability distribution of actual durations of first tuning segments, the first tuning segments representative of lengths of time during which panelists accessed first media; modify the lengths associated with ones of the first tuning segments to generate second tuning segments having second durations; and estimate a time when a media device associated with a return path data (RPD) device is powered on based on (i) the probability distribution, (ii) the second tuning segments, and (iii) a third tuning segment during which the RPD device accessed second media, the third tuning segment reported from the RPD device.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 17/211,289, which was filed on Mar. 24, 2021, as a continuation ofU.S. patent application Ser. No. 16/566,354, which was filed on Sep. 10,2019, as a continuation of U.S. patent application Ser. No. 15/639,164,which was filed on Jun. 30, 2017, and which claims the benefit of U.S.Provisional Patent Application No. 62/428,487, which was filed on Nov.30, 2016. U.S. patent application Ser. Nos. 17/211,289; 16/566,354;15/639,164; and U.S. Provisional Patent Application No. 62/428,487 arehereby incorporated herein by reference in their entireties. Priority toU.S. patent application Ser. Nos. 17/211,289; 16/566,354; 15/639,164;and U.S. Provisional Patent Application No. 62/428,487 is claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement and, moreparticularly, to methods and apparatus to model on/off states of mediapresentation devices based on return path data.

BACKGROUND

Many people access media through set top boxes (STBs) or other mediapresentation devices provided by media content providers (e.g., cablemedia providers, satellite media providers, etc.). Some STBs areequipped to report tuning information indicative of the media accessedby the STBs back to the content providers. Tuning information reportedback to content providers via STBs or other similar devices is sometimesreferred to as return path data (RPD). RPD tuning information may beused by audience measurement entities to track or monitor people'sexposure to media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example environment in which the teachings disclosed hereinmay be implemented.

FIG. 2 is an example implementation of the example audience measurementmodule of FIG. 1 .

FIG. 3 is a schematic representation of panel tuning informationcorresponding to media played on multiple different media sets over aperiod of time.

FIGS. 4-6 illustrate how RPD tuning information may be modelled based onthe panel tuning information of FIG. 3 .

FIG. 7 illustrates example cumulative distributions of durations paneltuning segments and modelled tuning segments.

FIG. 8 illustrates example cumulative distributions of durations oftail-end panel tuning segments and associated modelled tuning segments.

FIGS. 9-15 are flowcharts representative of example machine readableinstructions that may be executed by one or more processors to implementthe example audience measurement module of FIGS. 1 and/or 2 .

FIG. 16 is a schematic illustration of an example processing system thatmay be used and/or programmed to execute the example machine-readableinstructions of FIGS. 9-15 to implement the example audience measurementmodule of FIGS. 1 and/or 2 .

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

As used herein, an RPD device refers to any type of device (e.g., a STBor other similar device) that is capable of accessing media from acontent provider and reporting tuning information regarding the mediaaccessed back to the content provider. Such tuning information isreferred to herein as RPD tuning information or simply RPD. RPD devicesare often standalone devices that connect to separate media presentationdevices, such as, television sets, radios, smartphones, tablets,computers, or any other device capable of playing the media accessed bythe RPD device. Media presentation devices that play media accessed byassociated RPD devices are referred to herein as media sets or simplysets for purposes of brevity.

In some instances, a media set and an RPD device may be integrated intoa single device. However, when a media set and an associated RPD deviceare separate devices, it is possible for one to be powered on while theother is turned off. As a result, while RPD tuning information reportedby an RPD device provides data indicative of media accessed by the RPDdevice, such data is not necessarily indicative of media being playedfor consumption on the associated media set where the RPD device islocated. For example, after people watch a show on their television set(e.g., a media set) accessed via a connected STB (e.g., an RPD device),they may turn off their television set without turning off theassociated STB. In such a situation, the STB will continue to accessmedia provided on the station to which the STB was last tuned andcontinue to report such as RPD tuning information despite the fact thatno media is being played in the household because the television set isturned off. Therefore, RPD tuning information may be unreliable todetermine the media to which people are exposed unless the on/off stateof the associated media set can be determined.

Examples disclosed herein calculate capped durations for RPD tuningsegments reported from an RPD device that are estimated to correspond toperiods of time when an associated media set is turned on. As usedherein, an RPD tuning segment refers to a period of time during which anRPD device is accessing media from a particular source of media (e.g.,is tuned to a particular station or channel). Thus, each time a stationor channel is changed on the RPD device corresponds to the ending of oneRPD tuning segment and the beginning of a second different RPD tuningsegment. RPD tuning segments may also end if the RPD device is poweredoff or enters a standby mode. Likewise, an RPD tuning segment beginswhenever the RPD device is powered on or otherwise is removed fromstandby mode. The capped durations estimated in the disclosed exampleswill typically be at the front-end of an RPD tuning segment withunretained portions of the tuning segment extending thereafter becausepeople are known to turn off their media sets while leaving associatedRPD devices powered on. However, it may be possible that a cappedduration corresponds to the tail-end of an RPD tuning segment whenpeople turn on their media sets to begin playing media to which an RPDdevice that was left powered on was already accessing. Accordingly,examples disclosed herein calculated both front-end and tail-end cappeddurations for RPD tuning segments that may be used to estimate theperiod(s) of time when an associated media set is powered on andactually playing the media accessed by the RPD device.

The period(s) of time when a media set is on, as calculated fromfront-end and tail-end capped durations estimated from RPD tuninginformation collected from an associated RPD device, solves a problemthat arises from the current state of technology in which associatedmedia sets and RPD devices may be independently turned off or poweredon. This state of technology gives rise to the situation where an RPDdevice is accessing media and transmitting associated RPD tuninginformation despite the fact that the media is not being played on anassociated media set. As a result, audience measurements based on RPDtuning information are unreliable because it may not represent the mediato which people were actually exposed. Examples disclosed herein resolvethis problem by modelling RPD tuning segments to accurately predict whenmedia sets are turned on and, thus, playing the media accessed by theassociated RPD devices. This improves audience measurement metricsbecause it enables the use of RPD tuning information as a reliablesource of audience exposure to media thereby significantly expanding thepopulation from which audience measurement data is available beyond therelatively limited population pools of audience measurement panelists.

FIG. 1 is an example environment 100 in which the teachings disclosedherein may be implemented. In the illustrated example, a contentprovider 102 provides media to content subscribers and collects RPDtuning information indicative of the subscribers accessing the media.The content provider 102 may provide the RPD tuning information to anaudience measurement entity (AME) 104 (e.g., the Nielsen Company (US)LLC) to enable to the AME 104 to generate audience measurement metrics.In some examples, the content provider 102 and the AME 104 communicatevia a network 106 such as, for example, the Internet.

As shown in FIG. 1 , the example environment 100 includes a non-panelisthousehold 108 and a panelist household 110. Some of the subscribers tothe services of the content provider 102 may be people that have agreedto participate as panelists in a research study administered by the AME104. Thus, the panelist household 110 of FIG. 1 represents a householdthat includes one or more individuals that have subscribed to thecontent provider 102 and that have enrolled as a panelist with the AME104. In some examples, panelists correspond to a statistically selectedsubset of all potential audience members that is representative of awhole population of interest. In some such panel-based monitoringsystems, the panelists agree to provide detailed demographic informationabout themselves. In this manner, detailed exposure metrics aregenerated based on collected media exposure data and associated userdemographics, which can then be statistically extrapolated to an entirepopulation of interest (e.g., a local market, a national market, ademographic segment, etc.).

Both the non-panelist household 108 and the panelist household 110include an RPD device 112, 114. The RPD devices 112, 114 may be providedby the content provider 102 to enable access to media generated bycontent provider 102. Further, the RPD devices 112, 114 are capable ofreporting RPD tuning information back to the content provider 102indicative of the media being accessed by the RPD devices 112, 114. Insome examples, the RPD devices 112, 114 access media from the contentprovider 102 and report RPD tuning information to the content provider102 via the network 106. Each of the RPD devices 112, 114 is connectedto a corresponding media set 116, 118 to play the media accessed by theRPD devices 112, 114. In the illustrated example, the RPD devices 112,114 are separate from the corresponding media sets 116, 118 such thatthe media sets 116, 118 may be powered off while the RPD devices 112,114 remain powered on. As a result, RPD tuning information reported bythe RPD devices 112, 114 may not be representative of media that isactual played on the corresponding media sets 116, 118.

The panelist household 110 is provided with a metering device 120 totrack and/or monitor the media played on the media set 118 and reportsuch to the AME 104 (e.g., via the network 106). In some examples, themetering device 120 also tracks and reports who is being exposed to themedia being played so that the media exposure can be associated withparticular individuals and their associated demographics previouslycollected when the household members enrolled as panelists. While theduration of media tuning segments actually played on the media sets 116,118 cannot be directly confirmed from reported RPD tuning informationbecause such information does not indicate whether the media sets 116,118 are on or off, the audience measurement data reported by themetering device 120 does indicate the on/off state of the media set 118.As a result, the AME 104 is able to know the actual duration of mediatuning segments played on the media set 118 of the panelist household110. However, there is no direct way for the AME 104 to know the actualduration of media tuning segments played on the media set 116 of thenon-panelist household 108.

In some examples, the AME 104 includes an audience measurement module122 to predict the on/off state of the media set 116 of the non-panelisthousehold 108 as described more fully below. Briefly, the audiencemeasurement module 122 uses the actual duration of media tuning segmentsplayed in panelist households (e.g., the panelist household 110), asreported from metering devices (e.g., the metering device 120)monitoring such, to generate a model of RPD tuning segments that wouldbe expected if media being accessed was reported by RPD devices (e.g.,the RPD devices 112, 114). Based on the resulting modelled tuningsegments and a knowledge of the actual durations of tuning segmentsplayed on media devices in panelist households, the example audiencemeasurement module 122 may estimate capped durations for the RPD tuningsegments reported from RPD devices 112 in non-panelist households thatcorrespond to when associated media sets 116 in such households arelikely to be powered on. Based on these capped durations of RPD tuningsegments, reliable audience measurement metrics may be generated fornon-panelist households.

FIG. 2 is an example implementation of the example audience measurementmodule 122 of FIG. 1 . The example audience measurement module 122includes an example communications interface 202, an example paneltuning information database 204, an example RPD tuning informationdatabase 206, an example RPD model generator 208, an exampledistribution generator 210, an example correlation calculator 212, anexample RPD tuning information analyzer 214, an example set-on timecalculator 216, and an example report generator 218.

The example audience measurement module 122 is provided with the examplecommunications interface 202 to communicate with the metering device 120installed in the panelist household 110. That is, the metering device120 may report audience measurement data to the AME 104 that is receivedby the communications interface 202. The communications interface 202may receive audience measurement data from other panelist households notrepresented in the illustrated example. The collected audiencemeasurement data includes panel tuning information, which may be storedin the panel tuning information database 204. The panel tuninginformation may include an indication of the media played on the mediasets 118 in the panelist household 110. In some example, the media maybe uniquely identified. In other examples, the panel tuning informationmay identify a particular source of media (e.g., a station ID) fromwhich the particular media may be identified. The panel tuninginformation may also include timestamps and/or other forms of timinginformation indicative of the start time and end time of particularmedia tuning segments played on the media sets 118 of panelists.Inasmuch as the panel tuning information is based on media actual playedon the media sets of panelist homes, the panel tuning information willbe limited to periods of time when the media set 118 is powered on andactually playing media. Each distinct period of time during which amedia set 118 in the panelist household 110 is playing media associatedwith a particular source (e.g., a particular station or channel to whichthe RPD device 114 is tuned) is referred to herein as a panel tuningsegment. A panel tuning segment is distinct from an RPD tuning segmentdefined above in that panel tuning segments are tied to times when theassociated media set is powered on and actually playing media.

Additionally, in the illustrated example, the communications interface202 of the audience measurement module 122 receives RPD tuninginformation from the content provider 102. The content provider 102collects the RPD tuning information reported from RPD devices (e.g., theRPD devices 112, 114) accessing media content provided by the contentprovider 102. In some examples, the communications interface 202 mayreceive the RPD tuning information directly from the RPD devices 112,114 independent of communications between the AME 104 and the contentprovider 102. The RPD tuning information may be stored in the RPD tuninginformation database 206. Similar to the panel tuning information, theRPD tuning information includes a media identifier (e.g., a uniqueidentifier, a station ID, etc.) to identify the media accessed by theRPD devices. Further, the RPD tuning information includes timinginformation indicative of a start time and end time of RPD tuningsegments. Inasmuch as the RPD devices 112, 114 may be separately poweredfrom the associated media sets 116, 118, it is possible that someportions of the collected RPD tuning segments correspond to media thatwas never actually played on a media set 116, 118 (e.g., when the RPDtuning device 112, 114 is on and reporting tuning information while thecorresponding media set 116, 118 is turned off).

The example audience measurement module 122 is provided with the exampleRPD model generator 208 to generate a model of RPD tuning informationbased on predicted extensions of the durations of panel tuning segmentsreported in the collected panel tuning information. For example, FIG. 3is a schematic representation of panel tuning information 302corresponding to media played on multiple media sets 304, 306, 308, 310,312, 314 over a period of time. In the illustrated example, the mediaplayed on each media set 304, 306, 308, 310, 312, 314 is provided to themedia set via an associated RPD device. In some examples, each media set304, 306, 308, 310, 312, 314 is associated with a different panelisthousehold. In other examples, a single panelist household may includemore than one of the media sets 304, 306, 308, 310, 312, 314. In anyevent, the panel tuning information 302 represented in FIG. 3 is basedon data collected from associated metering devices 120 in the panelisthousehold(s) 110 and independent of RPD tuning information reported bythe associated RPD devices 114. That is, the panel tuning information302 represents the actual duration and timing of media played on eachrespective media set 304, 306, 308, 310, 312, 314 as reported by anassociated metering device 120.

Individual panel tuning segments of media played on each media set 304,306, 308, 310, 312, 314 are represented in FIG. 3 by individual boxeswith the dashed lines indicative of when the corresponding media set waspowered off or otherwise not playing media. The different shading and/orcross-hatching within the boxes represent different media sources (e.g.,stations, channels, etc.) to which the associated RPD devices 114 aretuned to provide the media. Thus, during the represented period, thefirst media set 304 is associated with two panel tuning segments 320,322 separated by a gap in time indicating the first media set 304 wasturned off between the two tuning segments 320, 322. Separate paneltuning segments are not necessarily spaced in time. For example, thesecond media set 306 includes two panel tuning segments 324, 326 inwhich the second panel tuning segment 326 immediately follows the firstpanel tuning segment 324 indicating the audience member changed thechannel or station to which the RPD device 114 was tuned.

As mentioned above, the example RPD model generator 208 may use thepanel tuning information 302 to model expected RPD tuning information bymodelling durations for the panel tuning segments reported in thecollected panel tuning information that are extended beyond the actualduration of the panel tuning segments. It may initially be assumed thatRPD devices 114 are always powered on. In such situations, an RPD devicewould continually report RPD tuning information such that the reportedRPD tuning segments would always appear to be contiguous with eachtuning segment ending when a new tuning segment begins. This situationis represented in FIG. 4 in which the shading and cross-hatching of theactual panel tuning segments have been extended out indefinitely asmodelled RPD tuning segments. Thus, the first panel tuning segment 320played on the first media set 304 is assumed to extend to the beginningof the second panel tuning segment 322 to model a first tuning segment402 associated with the first media set 304. A second modelled tuningsegment 404 corresponds to the second panel tuning segment 322 of FIG. 3but has been extended to the end of the period of time represented inthe figure.

In actual implementation, most RPD devices 114 do not remain onindefinitely as represented in FIG. 4 . Rather, many RPD devices 114include a standby timer that will cause the RPD devices 114 to stopreporting RPD tuning information if there has been no activity for a setamount of time. The particular length of a standby timer will likely bedifferent for different RPD devices 114 and/or different contentproviders 102. In some examples, the content provider 102 may providethe AME 104 with the standby timer length for the RPD devices 114associated with the content provider 102. In other examples, the standbytimer length may be determined from an analysis of RPD tuninginformation aggregated from multiple RPD devices 114 of the same type(and/or associated with the same content provider 102). Moreparticularly, the AME 104 may determine the standby timer by identifyingpeaks in a distribution of durations of reported RPD tuning segments.

FIG. 5 illustrates how the example RPD model generator 208 may reducethe indefinite-duration modelled tuning segments of FIG. 4 based on anexample standby timer length 502. The second RPD tuning segment 404 ofthe first media set 304, as represented in FIG. 5 , is shorter thaninitially assumed in FIG. 4 because, under the model, the tuninginformation is expected to stop after the standby timer length haselapsed following the end of the panel tuning segment 322. By contrast,shortening the modelled tuning segments by the standby timer length 502does not affect the modelled duration of the first modelled tuningsegment 402 of the first media set 304 because the time gap between thefirst panel tuning segment 320 and the second panel tuning segment 322is less than the standby timer length 502.

Typically, a standby timer is enabled by default in an RPD device 114.However, some users may manually disable the standby timer. Accordingly,in some examples, the RPD model generator 208 randomly selects aproportion of media sets 304, 306, 308, 310, 312, 314 as beingassociated with RPD devices 114 for which the standby timer is disabled.The RPD model generator 208 will not shorten the modelled tuningsegments for such media sets but will leave them to extend forward intime indefinitely (or until a subsequent panel tuning segment occurs).This is represented in FIG. 5 in connection with the fifth media set 312in which a modelled tuning segment 504 is modelled with a durationextending far beyond the standby timer length 502 while all othermodelled tuning segments 504 have been shortened or capped. Althoughdescribed as being extended indefinitely, in some examples, the RPDmodel generator 208 may model tuning segments associated with RPDdevices 114 with disabled standby timers by extending the panel tuningsegments for a finite but substantial extension period (e.g., 12 hours,24 hours, 48 hours, etc.).

In some examples, the number or proportion of media sets identified tobe associated with RPD devices 114 that have disabled the standby timeris derived from an analysis of aggregated RPD tuning information in asimilar manner to the derivation of the standby timer length byidentifying outliers on a distribution of durations of RPD tuningsegments. Thus, for example, if it is determined that 85% of all RPDdevices have a standby timer that is enabled, the RPD model generator208 may select the panel tuning segments associated with 15% of themedia sets 304, 306, 308, 310, 312, 314 to be modelled as beingassociated with an RPD device that does not have an enabled standbytimer. In other examples, the RPD model generator 208 may generate arandom number between 0 and 1 for each media set 304, 306, 308, 310,312, 314 and designate the media set as either associated with a standbyenabled RPD device or a standby disabled device depending on whether thenumber is above or below 85%.

In some examples, the RPD model generator 208 also accounts for thepossibility that some people will turn off their RPD devices at the sametime that they turn off an associated media set. In such situations, anRPD tuning segment would not be extended beyond a corresponding paneltuning segment but limited to the same duration as the panel tuningsegment. Accordingly, in some examples, the RPD model generator 208randomly selects a proportion of the panel tuning segments ascorresponding to a time when the associated RPD device 114 was turnedoff based on the probability that people turn off an RPD device 114. Forexample, assuming the probability that an RPD device 114 is powered offat the same time an associated media set is powered off is 50%, the RPDmodel generator may generate a random number between 0 and 1 for eachpanel tuning segment and then either extend the duration for a modelledtuning segment as shown in FIG. 5 or truncate the modelled duration tobe coextensive with the panel tuning segment based on whether the numberis above or below 50%. This is represented in FIG. 6 in which the firstmodelled tuning segment 402 for the first media set 304 is reduced tothe same duration as the first panel tuning segment 320 of the firstmedia set 304. By contrast, the second modelled tuning segment 404 ofthe first media set 304 remains with an extended modelled duration inFIG. 6 because the RPD model generator 208 randomly determined theassociated RPD device 114 was not turned off at the end of the secondpanel tuning segment 322. Modelled tuning segments that have the sameduration as the actual duration of an associated panel tuning segment(e.g., the first modelled tuning segment 402 of FIG. 6 or a modelledtuning segment associated with the panel tuning segment 326 of FIG. 3 )are referred to herein as non-extended model segments.

In some instances, the RPD model generator 208 may combine or mergemultiple model tuning segments associated with separate panel tuningsegments into a single modelled tuning segment when the model tuningsegments are contiguous and associated with a single source of media.This may occur when the separate panel tuning segments correspond tosuccessive tuning segments accessed by an RPD device 114 that areassociated with a single source of media but spaced by a gap in timecorresponding to when the associated media set 118 was turned off. Anexample situation where this may occur is when panelists turn off theirtelevisions after watching the evening news and then turn the televisionback on the next morning to catch the morning news without ever changingthe channel. This scenario is represented in connection with the sixthmedia set 314 of FIGS. 3-6 . In particular, first and second paneltuning segments 328, 330 of the sixth media set 314 are shown in FIG. 3as being two separate tuning segments associated with the same source ofmedia but spaced apart in time. As the RPD model generator 208 modelsthe extended duration for the associated modelled tuning segments asoutlined above, the two distinct panel tuning segments 328, 330 resultin one merged modelled tuning segment 602 as represented in FIG. 6because separate modelled tuning segments identified for each paneltuning segment 328, 330 would be contiguous (e.g., one segment ends atthe same time that the next segment begins). While the combined modelledtuning segment 602 corresponds to two panel tuning segments 328, 330 inthe illustrated example, the modelled tuning segment 602 could includeany number of successive panel tuning segments 328, 330.

Returning to FIG. 2 , the example audience measurement module 122 isprovided with the example distribution generator 210 to generate acumulative distribution of durations of the panel tuning segmentsreported in panel tuning information (e.g., the panel tuning information302 represented in FIG. 3 ) collected from metering devices 120 inpanelist households 110. Additionally, the example distributiongenerator 210 may generate a cumulative distribution of durations of themodelled tuning segments (e.g., as represented in FIG. 6 ) based on thecollected panel tuning segments. In some examples, the distributiongenerator 210 may combine or merge the cumulative distributions ofdurations of both the panel tuning segments and the modelled tuningsegments into a single graph 700 as shown in FIG. 7 . In the illustratedexample, the dashed line 702 is a panel tuning segment distribution thatrepresents the probability distribution of the actual duration of paneltuning segments while the solid line 704 is a modelled tuning segmentdistribution that represents the probability distribution of thedurations of the modelled tuning segments.

In some examples, the distribution generator 210 generates multipledifferent graphs 700 corresponding to different dimensions of interest.That is, in some examples, the panel tuning segments and correspondingmodelled tuning segments are aggregated based on differentcharacteristics or dimensions such as, for example, the daypart when themedia was accessed, the day of week when the media was accessed (e.g.,whether on a weekend or a weekday), the station from which the media wasaccessed, the genre of the media, and/or the duration of the tuningsegments.

In some examples, the distribution generator 210 may also generate acumulative distribution of durations of tail-end panel tuning segments.As used herein, a tail-end panel tuning segment refers to the last paneltuning segment in a series of at least two successive panel tuningsegments associated with a single modelled tuning segment that wasmerged from at least two modelled tuning segments. That is, withreference to FIG. 6 , the second panel tuning segment 330 of the sixthmedia set 314 corresponds to a tail-end panel tuning segment because itis the last panel tuning segment in the modelled tuning segment 602. Insome examples, the distribution generator 210 may combine or merge thecumulative distributions of durations of the tail-end panel tuningsegments with 1 minus the modelled tuning segment distribution (e.g.,the distribution 704 of FIG. 7 ) into a single graph 800 as shown inFIG. 8 . In the illustrated example, the dashed line 802 is a tail-endpanel tuning segment distribution that represents the probabilitydistribution of the actual duration of tail-end panel tuning segmentswhile the solid line 804 represents 1 minus the probability distribution704 of the modelled tuning segments. In some examples, the distributiongenerator 210 generates multiple different graphs 800 corresponding toeach different combination of dimensions of interest.

The example audience measurement module 122 of FIG. 2 is provided withthe example correlation calculator 212 to calculate a correlationcoefficient between the duration of the panel tuning segments and theduration of the corresponding modelled tuning segments. In someexamples, the correlation coefficient is a Pearson correlationcoefficient. In some examples, a different correlation coefficient iscalculated for each combination of particular dimensions of interest forthe tuning segments.

As described above, some of the modelled tuning segments arenon-extended model segments because they have the same duration as theactual panel tuning segment. For example, as shown in FIG. 6 , the firstpanel tuning segment 320 of the first media set 304 is the same durationas the corresponding non-extended model segment 402. In some examples,non-extended model segments are excluded in the calculation of thecorrelation coefficient.

In the illustrated example of FIG. 2 , the example audience measurementmodule 122 is provided with the example RPD tuning information analyzer214 to analyze RPD tuning information obtained from non-panelisthouseholds 108 to predict the actual duration of tuning segments towhich individuals in the non-panelist households 108 were exposed tomedia. In some examples, the RPD tuning information analyzer 214calculates capped durations for reported RPD tuning segments based onthe distributions represented in FIGS. 7 and 8 that estimate the periodsof time when the media sets 116 in non-panelist households 108 areturned on during the reported RPD tuning segments.

The RPD tuning information analyzer 214 may determine capped durationsfor reported RPD tuning segments based on either a probabilisticapproach or a deterministic approach. In the probabilistic approach, theRPD tuning information analyzer 214 generates a random number between 0and 1 and then identifies the corresponding duration from the paneltuning segment distribution 702 shown on the graph 700 of FIG. 7 . As aspecific example, assume that a particular reported RPD tuning segmentwas 125 minutes long and that the RPD tuning information analyzer 214generated the random number of 0.25. As shown in FIG. 7 , the randomnumber of 0.25 corresponds to a particular point 706 on the panel tuningsegment distribution 702. From the point 706 on the panel tuning segmentdistribution 702, a corresponding capped duration 708 of approximately53 minutes may be determined from the graph 700. Thus, using theprobabilistic approach, the RPD tuning information analyzer 214determines that the reported RPD tuning segment of 125 minutes is to beshortened or capped to a probabilistic-estimated duration of 53 minutes.

In the probabilistic approach, as outlined above, the capped durationfor the RPD tuning segment is based on random probabilities independentof the reported length of the RPD tuning segment. By contrast, thedeterministic approach uses the reported length of the reported RPDtuning information to estimate the capped duration for the segment. Inparticular, assuming an initial reported RPD tuning segment of 125minutes, a particular point 710 on the modelled tuning segmentdistribution 704 may be identified. As shown in the illustrated exampleof FIG. 7 , the point 710 corresponds to a probability of approximately0.42. The RPD tuning information analyzer 214 may use this value toidentify a corresponding point 712 on the panel tuning segmentdistribution 702. From the point 712 on the panel tuning segmentdistribution 702, a corresponding capped duration 714 of approximately71 minutes may be determined from the graph 700. Thus, using thedeterministic approach, the RPD tuning information analyzer 214determines that the reported RPD tuning segment of 125 minutes is to beshortened or capped to a deterministic-estimated duration of 71 minutes.

In some examples, the RPD tuning information analyzer 214 may estimate afinal capped duration for a reported RPD tuning segment based on aweighted average of the capped durations calculated using each of theprobabilistic approach and the deterministic approach described above.In some examples, the weighting of the two approaches is based on thecorrelation coefficient calculated by the correlation calculator 212 andthe proportion or fraction of non-extended model segments, relative toall modelled tuning segments, that were excluded from the correlationanalysis. More particularly, in some examples, the RPD tuninginformation analyzer 214 generates a random number between 0 and 1 for aparticular reported RPD tuning segment. If the random number is lessthan or equal to the proportion or fraction of the non-extended modelsegments excluded from the correlation analysis as described above, thecapped duration for a reported RPD tuning segment is determined tocorrespond to the reported duration of the reported RPD tuning segment.That is, the reported RPD tuning segment is treated as a non-extendedmodel segment such that the duration of the tuning segment is assumed tocorrespond to the actual duration that media was played via acorresponding media set. For values of the random number greater thanthe fraction of non-extended model segments, the capped duration(D_(cap)) for the reported RPD tuning segment is calculated as follows:

D _(cap) =rD _(det)+(1−r)D _(prob)   (Eq. 1)

where r is the correlation coefficient, D_(det) is thedeterministic-estimated duration for the RPD tuning segment, D_(prob) isthe probabilistic-estimated duration for the RPD tuning segment. ForEquation 1 to work, when the correlation coefficient is determined to beless than 0, the value is set to 0.

In some examples, the RPD tuning information analyzer 214 selects eitherthe probabilistic approach or the deterministic approach to estimate acapped duration for a reported RPD tuning segment based on the length ofthe reported RPD tuning segment. In some examples, this is accomplishedby setting the correlation coefficient to either 0 or 1. In someexamples, only the deterministic approach may be implemented when thereported RPD tuning segments are relatively long (e.g., over 180minutes) while only the probabilistic approach may be implemented forrelatively short segments (e.g., less than 180 minutes).

The example estimates for a capped duration of a reported RPD tuninginformation described above (including the probabilistic-estimatedduration and the deterministic-estimated duration) correspond tofront-end capped durations of RPD tuning segments. That is, theshortened or capped durations estimated above predict the actualduration of media played on a particular media set 116 beginning at thestart time (i.e., the front-end) of the reported RPD tuning segment.However, as described above, in some examples, a single RPD tuningsegment may correspond to multiple sessions of media played on theparticular media set 116 that are separated by a gap in timecorresponding to when an associated media set playing media was turnedoff. Thus, in addition to estimating a front-end capped duration for areported RPD tuning session, the RPD tuning information analyzer 214 mayalso calculated a tail-end capped duration for the reported RPD tuningsegment. A tail-end capped duration for a reported RPD tuning segmentmay be calculated in much the same way as described above for thefront-end capped durations except that different probabilitydistributions are used. More particularly, for tail-end cappeddurations, the graph 800 is used rather than the graph 700. That is, aprobabilistic approach may be implemented by determining the tail-endcapped duration based on where a randomly generated number falls on thetail-end panel segment distribution 802 of FIG. 8 . Additionally oralternatively, a deterministic approach may be implemented byidentifying a point on the distribution 804 (representative of 1 minusthe modelled tuning segment distribution) to determine the durationassociated with a corresponding point on the tail-end panel segmentdistribution 802. Further still, in some examples, the two approachesmay be weight averaged based on a correlation coefficient calculatedbetween the tail-end panel tuning segments and the associated modelledtuning segments.

In the illustrated example of FIG. 2 , the audience measurement module122 is provided with the example set-on time calculator 216 to calculatethe particular times when a media set 116 is powered on based on theestimated front-end and tail-end capped durations for reported RPDtuning segments collected from an RPD device 112 associated with themedia set 116. The example audience measurement module 122 is providedwith the example report generator 218 to generate reports indicative ofaudience measurement metrics based on the RPD tuning informationcollected from non-panelist households 108 and the calculated set-ontimes of the media sets 116 in the non-panelist households 108. Forexample, the report generator 218 may generate a report crediting thenon-panelist household 108 with exposure to the media accessed by theassociated the RPD device 112 during the period(s) of time when thecorresponding media set 116 is estimated to be powered on based on RPDtuning information collected from the RPD device 112.

While an example manner of implementing the audience measurement module122 of FIG. 1 is illustrated in FIG. 2 , one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example communications interface 202, the example paneltuning information database 204, the example RPD tuning informationdatabase 206, the example RPD model generator 208, the exampledistribution generator 210, the example correlation calculator 212, theexample RPD tuning information analyzer 214, the example set-on timecalculator 216, the example report generator 218, and/or, moregenerally, the example audience measurement module 122 of FIG. 2 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample communications interface 202, the example panel tuninginformation database 204, the example RPD tuning information database206, the example RPD model generator 208, the example distributiongenerator 210, the example correlation calculator 212, the example RPDtuning information analyzer 214, the example set-on time calculator 216,the example report generator 218, and/or, more generally, the exampleaudience measurement module 122 could be implemented by one or moreanalog or digital circuit(s), logic circuits, programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example communications interface 202, the example panel tuninginformation database 204, the example RPD tuning information database206, the example RPD model generator 208, the example distributiongenerator 210, the example correlation calculator 212, the example RPDtuning information analyzer 214, the example set-on time calculator 216,and/or the example report generator 218 is/are hereby expressly definedto include a non-transitory computer readable storage device or storagedisk such as a memory, a digital versatile disk (DVD), a compact disk(CD), a Blu-ray disk, etc. including the software and/or firmware.Further still, the example audience measurement module 122 of FIG. 1 mayinclude one or more elements, processes and/or devices in addition to,or instead of, those illustrated in FIG. 2 , and/or may include morethan one of any or all of the illustrated elements, processes anddevices.

Flowcharts representative of example machine readable instructions forimplementing the audience measurement module 122 of FIGS. 1 and/or 2 isshown in FIGS. 9-15 . In this example, the machine readable instructionscomprise a program for execution by a processor such as the processor1612 shown in the example processor platform 1600 discussed below inconnection with FIG. 16 . The program may be embodied in software storedon a non-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 1612, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 1612 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowcharts illustrated in FIGS. 9-15 , many othermethods of implementing the example audience measurement module 122 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, a FieldProgrammable Gate Array (FPGA), an Application Specific Integratedcircuit (ASIC), a comparator, an operational-amplifier (op-amp), a logiccircuit, etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIGS. 9-15 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim lists anythingfollowing any form of “include” or “comprise” (e.g., comprises,includes, comprising, including, etc.), it is to be understood thatadditional elements, terms, etc. may be present without falling outsidethe scope of the corresponding claim. As used herein, when the phrase“at least” is used as the transition term in a preamble of a claim, itis open-ended in the same manner as the term “comprising” and“including” are open ended.

Turning in detail to the flowcharts, the example process of FIG. 9begins at block 902 where the example communications interface 202obtains panel tuning information indicative of media sets (e.g., themedia set 118) playing media accessed via associated RPD devices (e.g.,the RPD device 114). In some examples, the panel tuning information isobtained from metering devices 120 in panelist households 110. The paneltuning information may be stored in the panel tuning informationdatabase 204.

At block 904, the example RPD model generator 208 generates a model ofRPD tuning segments based on the panel tuning information. Furtherdetail regarding the implementation of block 904 is provided below inconnection with FIGS. 10 and 11 . At block 906, the example distributiongenerator 210 generates distributions of the panel tuning segments andthe modelled tuning segments. Further detail regarding theimplementation of block 906 is provided below in connection with FIG. 12.

At block 908, the example communications interface 202 obtains reportedRPD tuning information reporting RPD tuning segments. In some examples,the RPD tuning information is received from a content provider 102 thatcollected the data from RPD devices 112, 114. The RPD tuning informationmay be associated with panelist households 110 and/or non-panelisthouseholds 108. In some examples, the RPD devices 112, 114 may reportthe RPD tuning information directly to the communications interface 202.

At block 910, the example RPD tuning information analyzer 214 estimatesfront-end capped durations for the reported RPD tuning segments. Furtherdetail regarding the implementation of block 910 is provided below inconnection with FIG. 13 . At block 912, the example RPD tuninginformation analyzer 214 estimates tail-end capped durations for thereported RPD tuning segments. Further detail regarding theimplementation of block 912 is provided below in connection with FIG. 14.

At block 914, the example set-on time calculator 216 calculates a set-ontimes for the media sets associated with the RPD tuning information.Further detail regarding the implementation of block 914 is providedbelow in connection with FIG. 15 . At block 916, the examplecommunications interface 202 determines whether there is more audienceRPD tuning information. If so, control returns to block 908. Otherwise,control advances to block 918 where the example report generator 218generates audience measurement reports. Thereafter, the example processof FIG. 9 ends.

FIG. 10 illustrates an example process to implement block 904 of FIG. 9. The example process begins at block 1002 where the example RPD modelgenerator 208 identifies panel tuning information associated with aparticular media set 118 in a panelist household 110. At block 1004, theexample RPD model generator 208 calculates the actual duration of apanel tuning segment played on the media set 118. While the media beingplayed may be accessed via an associated RPD device 114, the actualduration of the media being played is independently determined based onfeedback from the metering device 120 monitoring when the media set 118is powered on and what media is being played.

At block 1006, the example RPD model generator 208 calculates a modelledduration for a modelled tuning segment by extending the actual durationa substantial extension period. In some examples, the substantialextension period is a period of time that extends forward indefinitelyuntil a subsequent panel tuning segment occurs. In other examples, thesubstantial extension period is any suitable period of time that issignificantly longer than a typical duration for a person to consumemedia (e.g., 12 hours, 24 hours, 48 hours, etc.).

At block 1008, the example RPD model generator 208 determines whether totreat a standby timer as enabled on the associated RPD device 114. Asdescribed above, standby timers are typically enabled by default butsome users may manual disable the timers. In some examples, whether theRPD model generator 208 treats the standby timer as enabled is based onwhether a randomly generated number between 0 and 1 falls above or belowa percentage probability that users of the particular type of RPD device114 disable the standby timer. If the example RPD model generator 208determines to treat the standby timer as enabled, control advances toblock 1010 where the example RPD model generator 208 reduces themodelled duration according to a standby timer length. That is, ratherthan extend the actual duration of the panel tuning segment asubstantial extension period (e.g., an indefinite period of time), themodelled duration is limited to the length of the standby timer.Thereafter, control advances to block 1012. If the example RPD modelgenerator 208 determines to not treat the standby timer as enabled(block 1008), control advances directly to block 1012.

At block 1012, the example RPD model generator 208 determines whether toassume the RPD device 114 is shut off after the panel tuning segment. Insome examples, whether the RPD model generator 208 assumes the RPDdevice is shut off is based on whether a randomly generated numberbetween 0 and 1 falls above or below a percentage probability that usersof the particular type of RPD device 114 shut off the RPD device 114when they turn off their associated media sets. If the example RPD modelgenerator 208 determines to assume the RPD device 114 is shut off,control advances to block 1014 where the example RPD model generator 208reduces the modelled duration to the actual duration. That is, theduration of the modelled tuning segment is assumed to be the same as theactual duration of the corresponding panel tuning segment. In otherwords, the modelled tuning segment is designated as a non-extended modelsegment. After reducing the RPD duration (block 1014), control advancesto block 1016. If the example RPD model generator 208 determines not toassume the RPD device 114 is shut off, control advances directly toblock 1016.

At block 1016, the example RPD model generator 208 determines whetherthere is another panel tuning segment associated with the particularmedia set. In some examples, the number of panel tuning segmentsassociated with a particular set depends upon the period of time beinganalyzed to generate the model. In some examples, the RPD modelgenerator 208 generates new models each day so that the modelscorrespond to current data. In some such examples, the models generatedfor a particular day are based on RPD tuning information spanningmultiple days surrounding the particular day of interest. For example,the RPD model generator 208 may analyze panel tuning segments reportedduring a four-week period (28) ending on the day of interest. In someexamples, the period of time over which the panel tuning segments areanalyzed may include data collected one or more days following theparticular day of interest. In any event, if the example RPD modelgenerator 208 determines there is another panel tuning segmentassociated with the particular media set to be analyzed, control returnsto block 1004. Otherwise, control advances to block 1018 where theexample RPD model generator 208 calculates tail-end durations for themodelled tuning segments. Further detail regarding the implementation ofblock 1018 is provided below in connection with FIG. 11 . Aftercalculating the tail-end durations (block 1018), control advances toblock 1020 where the example RPD model generator 208 determines whetherthere is another media set. If so, control returns to block 1002.Otherwise, the example process of FIG. 10 ends and returns to completethe process of FIG. 9 .

FIG. 11 illustrates an example process to implement block 1018 of FIG.10 . The example process begins at block 1102 where the example RPDmodel generator 208 determines whether a panel tuning segment is part ofa series of consecutive panel tuning segments associated with the samemedia source and contiguous modelled tuning segments. If the example RPDmodel generator 208 determines that a panel tuning segment is not partof a series of consecutive panel tuning segments, control advances toblock 1104. At block 1104, the example RPD model generator 208 sets thetail-end duration for the associated modelled tuning segment to zero.Thereafter, control advances to block 1112 where the example RPD modelgenerator 208 determines whether there is another panel tuning segmentto analyze.

Returning to block 1102, if the example RPD model generator 208determines that a panel tuning segment is part of a series ofconsecutive panel tuning segments, control advances to block 1106. Atblock 1106, the example RPD model generator 208 merges the modelleddurations associated with the consecutive panel tuning segments. Inother words, the adjacent modelled tuning segments are combined andtreated as a single RPD tuning segment. At block 1108, the example RPDmodel generator 208 merges the actual durations of the consecutive paneltuning segments except for the last panel tuning segment in the series.That is, the duration of the consecutive panel tuning segments arecombined and treated as a single panel tuning segment. However, themerging of the panel tuning segments does not include the last paneltuning segment in the series. Thus, if the series includes only twoconsecutive segments, the merged actual duration would correspond to theactual duration of the first panel tuning segment. At block 1110, theexample RPD model generator 208 defines the tail-end duration for theassociated modelled tuning segment as the actual duration of the lastpanel tuning segment in the series.

At block 1112, the example RPD model generator 208 determines whetherthere is another panel tuning segment. If so, control returns to block1102. Otherwise, the example process of FIG. 11 ends and returns tocomplete the process of FIG. 10 .

FIG. 12 illustrates an example process to implement block 906 of FIG. 9. The example process begins at block 1202 where the exampledistribution generator 210 groups the panel tuning segments and themodelled tuning segments based on dimensions of interest. The dimensionsof interest may include daypart, day of week, station, genre, and/ortuning segment length.

At block 1204, the example distribution generator 210 selects a group ofthe tuning segments. At block 1206, the example distribution generator210 calculates the fraction of non-extended model segments relative toall modelled tuning segments. That is, as described above, some modelledtuning segments will have a modelled duration that is the same as theassociated panel tuning segment from which each tuning segment ismodelled. The proportion of these non-extended model segments relativeto the total number of modelled tuning segments is determined as thefraction at block 1206. At block 1208, the example distributiongenerator 210 calculates a correlation coefficient between the actualdurations and the modelled durations of the tuning segments except forthose associated with non-extended model segments. In some examples, thedistribution generator 210 may set the correlation coefficient to either0 or 1 (e.g., based on the duration of the tuning segments beinganalyzed).

At block 1210, the example distribution generator 210 generates a paneltuning segment distribution (e.g., the distribution 702 of FIG. 7 ). Atblock 1212, the example distribution generator 210 generates a modelledtuning segment distribution (e.g., the distribution 704 of FIG. 7 ). Atblock 1214, the example distribution generator 210 combines the paneland modelled distributions. At block 1216, the example distributiongenerator 210 calculates a tail-end panel tuning segment cumulativedistribution (e.g., the distribution 802 of FIG. 8 ). At block 1218, theexample distribution generator 210 combines the tail-end distributionand 1 minus the modelled distribution (e.g., the solid line 804 of FIG.8 ).

At block 1220, the example distribution generator 210 determines whetherthere is another group of tuning segments. If so, control returns toblock 1204. Otherwise, the example process of FIG. 12 ends and returnsto complete the process of FIG. 9 .

FIG. 13 illustrates an example process to implement block 910 of FIG. 9. The example process begins at block 1302 where the example RPD tuninginformation analyzer 214 calculates a duration of a reported RPD tuningsegment. Typically, RPD tuning information includes start times and endtimes for different tuning segments. Accordingly, the example RPD tuninginformation analyzer 214 calculates the duration by subtracting thestart time from the end time of the particular RPD tuning segment beinganalyzed.

At block 1304, the example RPD tuning information analyzer 214calculates a probabilistic-estimated duration for the reported RPDtuning segment. This is accomplished by randomly generating a number anddetermining the corresponding duration on a panel tuning segmentdistribution associated with panel tuning segments sharing the samedimensions as the reported RPD tuning segment. At block 1306, theexample RPD tuning information analyzer 214 calculates adeterministic-estimated duration for the reported RPD tuning segment.This is accomplished by determining the probability percentage on amodelled tuning segment distribution corresponding to the duration ofthe reported RPD tuning segment and then determining the correspondingduration on an associated panel tuning segment distribution.

At block 1308, the example RPD tuning information analyzer 214determines whether to treat the reported RPD tuning segment as anon-extended model segment. In some examples, the RPD tuning informationanalyzer 214 accomplishes this by comparing a randomly generated numberto the fraction of non-extended model segments relative to all modelledtuning segments calculated at block 1206 of FIG. 12 . If the example RPDtuning information analyzer 214 determines to treat the reported RPDtuning segment as a non-extended model segment, control advances toblock 1310 where the example RPD tuning information analyzer 214 setsthe front-end capped duration for the RPD tuning segment as the reportedRPD duration. Thereafter, control advances to block 1314 where theexample RPD tuning information analyzer 214 determines whether there isanother RPD tuning segment. If the example RPD tuning informationanalyzer 214 determines to not treat the reported RPD tuning segment asa non-extended model segment (block 1308), control advances to block1312.

At block 1312, the example RPD tuning information analyzer 214calculates the front-end capped duration for the reported RPD tuningsegment based on a weighted average of the probabilistic-estimatedduration and the deterministic estimated duration. This is accomplishedusing the correlation coefficient calculated at block 1208 of FIG. 12 .In some examples, the correlation coefficient is set to either 0 or 1such that the front-end capped duration is based exclusively on one ofthe probabilistic or deterministic approaches. In some examples, theprocess of FIG. 13 may be simplified by only calculating the front-endcapped duration for the reported RPD tuning segment based on one of theprobabilistic or deterministic approaches while the other approach andthe weight averaging blocks are omitted.

After calculating the front-end capped duration at block 1312, controladvances to block 1314. If the example RPD tuning information analyzer214 determines there is another RPD tuning segment, control returns toblock 1302. Otherwise, the example process of FIG. 13 ends and returnsto complete the process of FIG. 9 .

FIG. 14 illustrates an example process to implement block 912 of FIG. 9. The example process begins at block 1402 where the example RPD tuninginformation analyzer 214 calculates a probabilistic-estimated tail-endduration for the reported RPD tuning segment. At block 1404, the exampleRPD tuning information analyzer 214 calculates a deterministic-estimatedtail-end duration for the reported RPD tuning segment. At block 1406,the example RPD tuning information analyzer 214 calculates the tail-endcapped duration for the reported RPD tuning segment based on a weightedaverage of the probabilistic-estimated and the deterministic-estimatedtail-end durations. In some examples, the correlation coefficient is setto either 0 or 1 such that the tail-end capped duration is basedexclusively on one of the probabilistic or deterministic approaches. Insome examples, the process of FIG. 14 may be simplified by onlycalculating the tail-end capped duration for the reported RPD tuningsegment based on one of the probabilistic or deterministic approacheswhile the other approach and the weight averaging blocks are omitted.

After calculating the tail-end capped duration at block 1406, controladvances to block 1408 where the example RPD tuning information analyzer214 determines whether there is another reported RPD tuning segment. Ifso, control returns to block 1402. Otherwise, the example process ofFIG. 14 ends and returns to complete the process of FIG. 9 .

FIG. 15 illustrates an example process to implement block 914 of FIG. 9. The example process begins at block 1502 where the example set-on timecalculator 216 identifies RPD tuning information from an RPD deviceassociated with a particular media set. At block 1504, the exampleset-on time calculator 216 selects an RPD tuning segment associated withthe RPD device. At block 1506, the example set-on time calculator 216determines whether the sum of the front-end capped duration and thetail-end capped duration is greater than or equal to the reported RPDduration. If so, control advances to block 1508 where the example set-ontime calculator 216 defines a set-on time for the media set ascorresponding to the reported RPD duration beginning at the start timeof the reported RPD tuning segment. Thereafter, control advances toblock 1516 where the example set-on time calculator 216 determineswhether there is another reported RPD tuning segment. If the exampleset-on time calculator 216 determines the sum of the front-end cappedduration and the tail-end capped duration is less than the reported RPDduration, control advances to block 1510.

At block 1510, the example set-on time calculator 216 defines a set-ontime for the media set as corresponding to the front-end capped durationbeginning at the start time of the reported RPD tuning segment. At block1512, the example set-on time calculator 216 determines whether thetail-end capped duration is greater than 0. If so, control advances toblock 1514 where the example set-on time calculator 216 generates a newtuning segment with a set-on time for the media set corresponding to thetail-end capped duration that ends at the end time of the reported RPDtuning segment. Thereafter, control advances to block 1516. If theexample set-on time calculator 216 determines that the tail-end cappedduration is not greater than 0 (block 1512), control advances directlyto block 1516.

At block 1516, the example set-on time calculator 216 determines whetherthere is another reported RPD tuning segment. If so, control returns toblock 1504. Otherwise, control advances to block 1518 where the exampleset-on time calculator 216 determines whether there is another mediaset. If so, control returns to block 1502. Otherwise, the exampleprocess of FIG. 15 ends and control returns to complete the process ofFIG. 9 .

FIG. 16 is a block diagram of an example processor platform 1600 capableof executing the instructions of FIGS. 9-15 to implement the audiencemeasurement module 122 of FIGS. 1 and/or 2 . The processor platform 1600can be, for example, a server, a personal computer, a mobile device(e.g., a cell phone, a smart phone, a tablet such as an iPad™), apersonal digital assistant (PDA), an Internet appliance, or any othertype of computing device.

The processor platform 1600 of the illustrated example includes aprocessor 1612. The processor 1612 of the illustrated example ishardware. For example, the processor 1612 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer. The hardware processor may be asemiconductor based (e.g., silicon based) device. In this example, theprocessor implements the example communications interface 202, theexample RPD model generator 208, the example distribution generator 210,the example correlation calculator 212, the example RPD tuninginformation analyzer 214, the example set-on time calculator 216, andthe example report generator 218.

The processor 1612 of the illustrated example includes a local memory1613 (e.g., a cache). The processor 1612 of the illustrated example isin communication with a main memory including a volatile memory 1614 anda non-volatile memory 1616 via a bus 1618. The volatile memory 1614 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1616 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1614,1616 is controlled by a memory controller.

The processor platform 1600 of the illustrated example also includes aninterface circuit 1620. The interface circuit 1620 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1622 are connectedto the interface circuit 1620. The input device(s) 1622 permit(s) a userto enter data and/or commands into the processor 1612. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1624 are also connected to the interfacecircuit 1620 of the illustrated example. The output devices 1624 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1620 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip and/or a graphics driver processor.

The interface circuit 1620 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1626 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1600 of the illustrated example also includes oneor more mass storage devices 1628 for storing software and/or data. Inthe illustrated example, the mass storage devices 1628 implements theexample panel tuning information database 204 and the example RPD tuninginformation database 206. Examples of such mass storage devices 1628include floppy disk drives, hard drive disks, compact disk drives,Blu-ray disk drives, RAID systems, and digital versatile disk (DVD)drives.

The coded instructions 1632 of FIGS. 9-15 may be stored in the massstorage device 1628, in the volatile memory 1614, in the non-volatilememory 1616, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that improvethe accuracy of estimating the on-off state of media sets based on RPDtuning information reported from RPD devices associated the media sets.This is important for accurate audience measurement metrics becausepeople should not be counted as audience members exposed to mediaaccessed by RPD devices unless the associated media set is powered onand actually playing the media being accessed and reported on in thecollected RPD tuning information. In this manner, more accurate audiencemeasurement metrics may be generated based on collected RPD tuninginformation.

Example 1 is an apparatus that includes a distribution generator,implemented via a processor, to generate a modelled tuning segmentdistribution indicative of modelled durations of modelled tuningsegments. The modelled tuning segments based on panel tuning segmentsduring which panelists were exposed to first media. The apparatusincludes a return path data (RPD) tuning information analyzer,implemented via the processor, to analyze RPD tuning informationreported from an RPD device. The RPD tuning information is indicative ofa reported RPD tuning segment during which the RPD device was accessingsecond media. The apparatus includes a set-on time calculator,implemented via the processor, to estimate a set-on time for a media setassociated with the RPD device based on the RPD tuning information andthe modelled tuning segment distribution. The set-on time is indicativeof a period of time when the media set is powered on.

Example 2 includes the subject matter of Example 1, wherein thedistribution generator is to generate a panel tuning segmentdistribution indicative of actual durations of the panel tuningsegments. The RPD tuning information analyzer is to determine aprobability percentage on the modelled tuning segment distributioncorresponding to a reported duration of the reported RPD tuning segmentand to determine a deterministic-estimated duration based on where theprobability percentage falls on the panel tuning segment distribution.The set-on time is estimated based on the deterministic-estimatedduration.

Example 3 includes the subject matter of Example 2, wherein the RPDtuning information analyzer is to determine a probabilistic-estimatedduration based on where a randomly generated number falls on the paneltuning segment distribution. The set-on time calculator is to determinethe set-on time based on a weighted average of thedeterministic-estimated duration and the probabilistic-estimatedduration.

Example 4 includes the subject matter of anyone of Examples 1-3 andfurther includes an RPD model generator to determine actual durations ofthe panel tuning segments, calculate modelled durations based on theactual durations of the panel tuning segments, and define the modelledtuning segments based on the modelled durations.

Example 5 includes the subject matter of Example 4, wherein the RPDmodel generator is to calculate the modelled durations by: extending theactual durations of the panel tuning segments by the shorter of (1) asubstantial extension period and (2) a gap in time between ones of thepanel tuning segments and a next subsequent panel tuning segment;reducing the modelled durations associated with a first portion of thepanel tuning segments based on a standby timer length associated withthe RPD device; and reducing the modelled durations associated with asecond portion of the panel tuning segments to the actual durations ofthe corresponding panel tuning segments of the second portion.

Example 6 includes the subject matter of Example 5, wherein the RPDmodel generator is to identify the first portion of the panel tuningsegments based on a comparison of a randomly generated number to aprobability that RPD devices used to access the first media have enabledstandby timers.

Example 7 includes the subject matter of anyone of Examples 5 or 6,wherein the RPD model generator is to identify the second portion of thepanel tuning segments based on a comparison of a randomly generatednumber to a probability that RPD devices used to access the first mediaare powered off at a same time that associated media sets are poweredoff.

Example 8 includes the subject matter of anyone of Examples 4-7, whereinthe RPD model generator is to identify different series of consecutiveones of the panel tuning segments associated with a same media sourceand associated with contiguous modelled tuning segments. The RPD modelgenerator to generate a tail-end panel tuning segment distributionindicative of actual durations of the last panel tuning segments in thedifferent series of consecutive panel tuning segments. The RPD tuninginformation analyzer to estimate a tail-end duration for the reportedRPD tuning segment based on the tail-end panel tuning segmentdistribution.

Example 9 includes the subject matter of Example 8, wherein the RPDmodel generator is to merge the contiguous modelled tuning segmentsassociated with the different series of consecutive panel tuningsegments into different single modelled tuning segments. The set-on timecalculator is to calculate a second set-on time for the media setcorresponding to the tail-end duration for the reported RPD tuningsegment when a reported duration for the reported RPD tuning segment isgreater than the sum of the tail-end duration for the reported RPDtuning segment and a front-end duration for the reported RPD tuningsegment.

Example 10 is a method that involves generating a modelled tuningsegment distribution indicative of modelled durations of modelled tuningsegments. The modelled tuning segments are based on panel tuningsegments during which panelists were exposed to first media. The methodincludes obtaining return path data (RPD) tuning information reportedfrom an RPD device. The RPD tuning information indicative of a reportedRPD tuning segment during which the RPD device was accessing secondmedia. The method includes estimating a set-on time for a media setassociated with the RPD device based on the RPD tuning information andthe modelled tuning segment distribution. The set-on time indicative ofa period of time when the media set is powered on.

Example 11 includes the subject matter of Example 10 and furtherincludes generating a panel tuning segment distribution indicative ofactual durations of the panel tuning segments, determining a probabilitypercentage on the modelled tuning segment distribution corresponding toa reported duration of the reported RPD tuning segment, and determininga deterministic-estimated duration for the set-on time based on wherethe probability percentage falls on the panel tuning segmentdistribution.

Example 12 includes the subject matter of Example 11 and furtherincludes determining a probabilistic-estimated duration for the set-ontime based on where a randomly generated number falls on the paneltuning segment distribution, and determining the set-on time based on aweighted average of the deterministic-estimated duration and theprobabilistic-estimated duration.

Example 13 includes the subject matter of 10, further including:

-   -   determining actual durations of the panel tuning segments,        calculating modelled durations based on the actual durations of        the panel tuning segments, and defining the modelled tuning        segments based on the modelled durations.

Example 14 includes the subject matter of 13, wherein the modelleddurations are calculated by: extending the actual durations of the paneltuning segments by the shorter of (1) a substantial extension period and(2) a gap in time between ones of the panel tuning segments and a nextsubsequent panel tuning segment, reducing the modelled durationsassociated with a first portion of the panel tuning segments based on astandby timer length associated with the RPD device, and reducing themodelled durations associated with a second portion of the panel tuningsegments to the actual durations of the corresponding panel tuningsegments of the second portion.

Example 15 includes the subject matter of 14, further includingidentifying the first portion of the panel tuning segments based on acomparison of a randomly generated number to a probability that RPDdevices used to access the first media have enabled standby timers.

Example 16 includes the subject matter of 14, further includingidentifying the second portion of the panel tuning segments based on acomparison of a randomly generated number to a probability that RPDdevices used to access the first media are powered off at a same timethat associated media sets are powered off.

Example 17 includes the subject matter of 13, further includingidentifying different series of consecutive ones of the panel tuningsegments associated with a same media source and associated withcontiguous modelled tuning segments, generating a tail-end panel tuningsegment distribution indicative of actual durations of the last paneltuning segments in the different series of consecutive panel tuningsegments, and estimating a tail-end duration for the reported RPD tuningsegment based on the tail-end panel tuning segment distribution.

Example 18 includes the subject matter of 17, further including mergingthe contiguous modelled tuning segments associated with the differentseries of consecutive panel tuning segments into different singlemodelled tuning segments, and calculating a second set-on time for themedia set corresponding to the tail-end duration for the reported RPDtuning segment when a reported duration for the reported RPD tuningsegment is greater than the sum of the tail-end duration for thereported RPD tuning segment and a front-end duration for the reportedRPD tuning segment.

Example 19 is a non-transitory computer readable medium comprisinginstructions that, when executed, cause a machine to at least generate amodelled tuning segment distribution indicative of modelled durations ofmodelled tuning segments. The modelled tuning segments is based on paneltuning segments during which panelists were exposed to first media. Theinstructs cause the machine to obtain return path data (RPD) tuninginformation reported from an RPD device. The RPD tuning informationindicative of a reported RPD tuning segment during which the RPD devicewas accessing second media. The instructions cause the machine toestimate a set-on time for a media set associated with the RPD devicebased on the RPD tuning information and the modelled tuning segmentdistribution. The set-on time is indicative of a period of time when themedia set is powered on.

Example 20 includes the subject matter of 19, wherein the instructionsfurther cause the machine to generate a panel tuning segmentdistribution indicative of actual durations of the panel tuningsegments, determine a probability percentage on the modelled tuningsegment distribution corresponding to a reported duration of thereported RPD tuning segment, and determine a deterministic-estimatedduration for the set-on time based on where the probability percentagefalls on the panel tuning segment distribution.

Example 21 includes the subject matter of 20, wherein the instructionsfurther cause the machine to determine a probabilistic-estimatedduration for the set-on time based on where a randomly generated numberfalls on the panel tuning segment distribution, and determine the set-ontime based on a weighted average of the deterministic-estimated durationand the probabilistic-estimated duration.

Example 22 includes the subject matter of anyone of Examples 19-21,wherein the instructions further cause the machine to determine actualdurations of the panel tuning segments, calculate modelled durationsbased on the actual durations of the panel tuning segments, and definethe modelled tuning segments based on the modelled durations.

Example 23 includes the subject matter of Example 22, wherein themodelled durations are calculated by: extending the actual durations ofthe panel tuning segments by the shorter of (1) a substantial extensionperiod and (2) a gap in time between ones of the panel tuning segmentsand a next subsequent panel tuning segment, reducing the modelleddurations associated with a first portion of the panel tuning segmentsbased on a standby timer length associated with the RPD device, andreducing the modelled durations associated with a second portion of thepanel tuning segments to the actual durations of the corresponding paneltuning segments of the second portion.

Example 24 includes the subject matter of Example 23, wherein theinstructions further cause the machine to identify the first portion ofthe panel tuning segments based on a comparison of a randomly generatednumber to a probability that RPD devices used to access the first mediahave enabled standby timers.

Example 25 includes the subject matter of anyone of Examples 23 or 24,wherein the instructions further cause the machine to identify thesecond portion of the panel tuning segments based on a comparison of arandomly generated number to a probability that RPD devices used toaccess the first media are powered off at a same time that associatedmedia sets are powered off

Example 26 includes the subject matter of anyone of Examples 22-25,wherein the instructions further cause the machine to: identifydifferent series of consecutive ones of the panel tuning segmentsassociated with a same media source and associated with contiguousmodelled tuning segments, generate a tail-end panel tuning segmentdistribution indicative of actual durations of the last panel tuningsegments in the different series of consecutive panel tuning segments,and estimate a tail-end duration for the reported RPD tuning segmentbased on the tail-end panel tuning segment distribution.

Example 27 includes the subject matter of Example 17, wherein theinstructions further cause the machine to merge the contiguous modelledtuning segments associated with the different series of consecutivepanel tuning segments into different single modelled tuning segments.The instructions further cause the machine to calculate a second set-ontime for the media set corresponding to the tail-end duration for thereported RPD tuning segment when a reported duration for the reportedRPD tuning segment is greater than the sum of the tail-end duration forthe reported RPD tuning segment and a front-end duration for thereported RPD tuning segment.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1. An apparatus comprising: memory; instructions; and processorcircuitry to execute the instructions to: generate a probabilitydistribution of actual durations of first tuning segments, the firsttuning segments representative of lengths of time during which panelistsaccessed first media; modify the lengths associated with ones of thefirst tuning segments to generate second tuning segments having seconddurations; and estimate a time when a media device associated with areturn path data (RPD) device is powered on based on (i) the probabilitydistribution, (ii) the second tuning segments, and (iii) a third tuningsegment during which the RPD device accessed second media, the thirdtuning segment reported from the RPD device.
 2. The apparatus of claim1, wherein the probability distribution is a first probabilitydistribution and the processor circuitry is to generate a secondprobability distribution based on the second tuning segments, theestimate of the time when a media device is powered on based on thesecond probability distribution.
 3. The apparatus of claim 1, whereinthe processor circuitry is to determine a correlation coefficientbetween the actual durations of the first tuning segments and seconddurations of the second tuning segments.
 4. The apparatus of claim 1,wherein the processor circuitry is to modify the lengths associated withones of the first tuning segments by: extending the actual durations ofcorresponding ones of the first tuning segments by a first amount; andreducing the extended durations associated with a subset of the firsttuning segments based on a second amount.
 5. The apparatus of claim 4,wherein the first amount corresponds to the shorter of (i) a substantialextension period and (ii) a gap in time between ones of the first tuningsegments and corresponding subsequent ones of the first tuning segments.6. The apparatus of claim 4, wherein the second amount corresponds to astandby timer length associated with the RPD device.
 7. The apparatus ofclaim 4, wherein the subset is a first subset, the processor circuitryis to reduce the extended durations associated with a second subset ofthe first tuning segments to the actual durations of the correspondingfirst tuning segments of the second subset.
 8. The apparatus of claim 7,wherein the first subset of the first tuning segments overlaps with thesecond subset of the first tuning segments.
 9. A non-transitory computerreadable medium comprising instructions that, when executed, causeprocessor circuitry to at least: generate a probability distribution ofactual durations of first tuning segments, the first tuning segmentsrepresentative of lengths of time during which panelists accessed firstmedia; modify the lengths associated with ones of the first tuningsegments to generate second tuning segments having second durations; andestimate a time when a media device associated with a return path data(RPD) device is powered on based on (i) the probability distribution,(ii) the second tuning segments, and (iii) a third tuning segment duringwhich the RPD device accessed second media, the third tuning segmentreported from the RPD device.
 10. The computer readable medium of claim9, wherein the probability distribution is a first probabilitydistribution and the instructions cause the processor circuitry togenerate a second probability distribution based on the second tuningsegments, the estimate of the time when a media device is powered onbased on the second probability distribution.
 11. The computer readablemedium of claim 9, wherein the instructions cause the processorcircuitry to determine a correlation coefficient between the actualdurations of the first tuning segments and second durations of thesecond tuning segments.
 12. The computer readable medium of claim 9,wherein the instructions cause the processor circuitry to modify thelengths associated with ones of the first tuning segments by: extendingthe actual durations of corresponding ones of the first tuning segmentsby a first amount; and reducing the extended durations associated with asubset of the first tuning segments based on a second amount.
 13. Thecomputer readable medium of claim 12, wherein the first amountcorresponds to the shorter of (i) a substantial extension period and(ii) a gap in time between ones of the first tuning segments andcorresponding subsequent ones of the first tuning segments.
 14. Thecomputer readable medium of claim 12, wherein the second amountcorresponds to a standby timer length associated with the RPD device.15. The computer readable medium of claim 12, wherein the subset is afirst subset, the instructions to cause the processor circuitry toreduce the extended durations associated with a second subset of thefirst tuning segments to the actual durations of the corresponding firsttuning segments of the second subset.
 16. The computer readable mediumof claim 15, wherein the first subset of the first tuning segmentsoverlaps with the second subset of the first tuning segments.
 17. Amethod comprising: generating a probability distribution of actualdurations of first tuning segments, the first tuning segmentsrepresentative of lengths of time during which panelists accessed firstmedia; modifying the lengths associated with ones of the first tuningsegments to generate second tuning segments having second durations; andestimating, by executing an instruction with processor circuitry, a timewhen a media device associated with a return path data (RPD) device ispowered on based on (i) the probability distribution, (ii) the secondtuning segments, and (iii) a third tuning segment during which the RPDdevice accessed second media, the third tuning segment reported from theRPD device.
 18. The method of claim 17, wherein the probabilitydistribution is a first probability distribution, the method furtherincluding generating a second probability distribution based on thesecond tuning segments, the estimate of the time when a media device ispowered on based on the second probability distribution.
 19. The methodof claim 17, further including determining a correlation coefficientbetween the actual durations of the first tuning segments and seconddurations of the second tuning segments.
 20. The method of claim 17,further including modifying the lengths associated with ones of thefirst tuning segments by: extending the actual durations ofcorresponding ones of the first tuning segments by a first amount; andreducing the extended durations associated with a subset of the firsttuning segments based on a second amount. 21-24. (canceled)